Decomposition of Error Components Between Angular, Forward, and Sideways Errors in Estimated Positions of a Computing Device

ABSTRACT

Examples include systems and methods for decomposition of error components between angular, forward, and sideways errors in estimated positions of a computing device. One method includes determining an estimation of a current position of the computing device based on a previous position of the computing device, an estimated speed over an elapsed time, and a direction of travel of the computing device, determining a forward, sideways, and orientation change error component of the estimation of the current position of the computing device, determining a weight to apply to the forward, sideways, and orientation change error components based on average observed movement of the computing device, and using the weighted forward, sideways, and orientation change error components as constraints for determination of an updated estimation of the current position of the computing device.

CROSS-REFERENCE TO RELATED APPLICATION

The present disclosure is a continuation of U.S. patent application Ser.No. 14/176,241, filed on Feb. 10, 2014, the entire contents of which areincorporated herein by reference.

BACKGROUND

Unless otherwise indicated herein, the materials described in thissection are not prior art to the claims in this application and are notadmitted to be prior art by inclusion in this section.

A location of a computing device can be determined using many differenttechniques including based either on Global Positioning System (GPS)data or on data associated with a wireless access point, such as acellular base station or an 802.11 access point. For example, a mobilecomputing device may receive a GPS signal and responsively determine itsposition on the face of the Earth (e.g. an absolute location). In adifferent example, a mobile computing device may receive a signal fromeither a cellular base station or an 802.11 access point. The cellularbase station or an 802.11 access point may estimate an exact location.Based on the location of either the cellular base station or an 802.11access point, the mobile computing device can calculate its exactposition.

Within some instances, a localization of a mobile computing device mayoccur via use of data from multiple different networks. Many locationbased services can be provided to a mobile computing device based ondetermining the location of the mobile computing device.

SUMMARY

In one example, a method is provided that comprises determining anestimation of a current position of the computing device based on aprevious position of the computing device, an estimated speed over anelapsed time, and a direction of travel of the computing device. Themethod further includes determining a forward error component of theestimation of the current position of the computing device, and theforward error component is indicative of error in the estimation of thecurrent position along a forward direction of travel of the computingdevice. The method also includes determining a sideways error componentof the estimation of the current position of the computing device, andthe sideways error component is indicative of error in the estimation ofthe current position along a sideways direction that is substantiallyperpendicular to the direction of travel of the computing device. Themethod also includes determining an orientation change error componentof the estimation of the current position of the computing device, andthe orientation change error component is indicative of error in theestimation of the current position due to a change in the direction oftravel of the computing device. The method also includes determining, bya processor, a weight to apply to the forward error component, thesideways error component, and the orientation change error componentbased on average observed movement of the computing device, and using,by the processor, the weighted forward error component, sideways errorcomponent, and orientation change error component as constraints fordetermination of an updated estimation of the current position of thecomputing device.

In another example, a computer readable memory having stored thereininstructions, that when executed by a computing device, cause thecomputing device to perform functions is provided. The functionscomprise determining an estimation of a current position of thecomputing device based on a previous position of the computing device,an estimated speed over an elapsed time, and a direction of travel ofthe computing device. The functions also comprise determining a forwarderror component of the estimation of the current position of thecomputing device, and the forward error component is indicative of errorin the estimation of the current position along a forward direction oftravel of the computing device. The functions also comprise determininga sideways error component of the estimation of the current position ofthe computing device, and the sideways error component is indicative oferror in the estimation of the current position along a sidewaysdirection that is substantially perpendicular to the direction of travelof the computing device. The functions also comprise determining anorientation change error component of the estimation of the currentposition of the computing device, and the orientation change errorcomponent is indicative of error in the estimation of the currentposition due to a change in the direction of travel of the computingdevice. The functions also comprise determining a weight to apply to theforward error component, the sideways error component, and theorientation change error component based on average observed movement ofthe computing device, and using the weighted forward error component,sideways error component, and orientation change error component asconstraints for determination of an updated estimation of the currentposition of the computing device.

In still another example, a computing device is provided that comprisesone or more processors, and data storage configured to storeinstructions that, when executed by the one or more processors, causethe computing device to perform functions. The functions comprisedetermining an estimation of a current position of the computing devicebased on a previous position of the computing device, an estimated speedover an elapsed time, and a direction of travel of the computing device.The functions also comprise determining a forward error component of theestimation of the current position of the computing device, and theforward error component is indicative of error in the estimation of thecurrent position along a forward direction of travel of the computingdevice. The functions also comprise determining a sideways errorcomponent of the estimation of the current position of the computingdevice, and the sideways error component is indicative of error in theestimation of the current position along a sideways direction that issubstantially perpendicular to the direction of travel of the computingdevice. The functions also comprise determining an orientation changeerror component of the estimation of the current position of thecomputing device, and the orientation change error component isindicative of error in the estimation of the current position due to achange in the direction of travel of the computing device. The functionsalso comprise determining a weight to apply to the forward errorcomponent, the sideways error component, and the orientation changeerror component based on average observed movement of the computingdevice, and using the weighted forward error component, sideways errorcomponent, and orientation change error component as constraints fordetermination of an updated estimation of the current position of thecomputing device.

In yet another example, a system is provided that comprises a means fordetermining an estimation of a current position of the computing devicebased on a previous position of the computing device, an estimated speedover an elapsed time, and a direction of travel of the computing device.The system further includes a means for determining a forward errorcomponent of the estimation of the current position of the computingdevice, and the forward error component is indicative of error in theestimation of the current position along a forward direction of travelof the computing device. The system also includes a means fordetermining a sideways error component of the estimation of the currentposition of the computing device, and the sideways error component isindicative of error in the estimation of the current position along asideways direction that is substantially perpendicular to the directionof travel of the computing device. The system also includes a means fordetermining an orientation change error component of the estimation ofthe current position of the computing device, and the orientation changeerror component is indicative of error in the estimation of the currentposition due to a change in the direction of travel of the computingdevice. The system also includes a means for determining a weight toapply to the forward error component, the sideways error component, andthe orientation change error component based on average observedmovement of the computing device, and a means for using the weightedforward error component, sideways error component, and orientationchange error component as constraints for determination of an updatedestimation of the current position of the computing device.

These as well as other aspects, advantages, and alternatives, willbecome apparent to those of ordinary skill in the art by reading thefollowing detailed description, with reference where appropriate to theaccompanying figures.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 illustrates an example communication system in which an examplemethod may be implemented.

FIG. 2 illustrates a schematic drawing of an example device.

FIG. 3 illustrates a schematic drawing of another example computingdevice.

FIG. 4 is a flow diagram illustrating an example method for determininga location or movement of a device.

FIG. 5 is a block diagram of an example method of determining errorcomponents of an estimation of position of a computing device, inaccordance with at least some embodiments described herein.

FIG. 6 is a conceptual diagram illustrating an example determination oferror components of an estimation of position of a computing device.

FIG. 7 is another conceptual diagram illustrating an exampledetermination of an updated estimation of position of a computingdevice.

DETAILED DESCRIPTION

The following detailed description describes various features andfunctions of the disclosed systems and methods with reference to theaccompanying figures. In the figures, similar symbols identify similarcomponents, unless context dictates otherwise. The illustrative systemand method embodiments described herein are not meant to be limiting. Itmay be readily understood that certain aspects of the disclosed systemsand methods can be arranged and combined in a wide variety of differentconfigurations, all of which are contemplated herein.

Referring now to the figures, FIG. 1 illustrates an examplecommunication system 100 in which an example method may be implemented.In FIG. 1, a client device 102 may communicate with a server 104 via oneor more wired and/or wireless interfaces. The client device 102 and theserver 104 may communicate within a network. Alternatively, the clientdevice 102 and the server 104 may each reside within a respectivenetwork.

The client device 102 may be any type of computing device or transmitterincluding a laptop computer, a mobile telephone, or tablet computingdevice, etc., that is configured to transmit data 106 to or receive data108 from the server 104 in accordance with the method and functionsdescribed herein. The client device 102 may include a user interface, acommunication interface, a processor, and data storage comprisinginstructions executable by the processor for carrying out one or morefunctions relating to the data sent to, or received by, the server 104.The user interface may include buttons, a touchscreen, a microphone,and/or any other elements for receiving inputs, as well as a speaker,one or more displays, and/or any other elements for communicatingoutputs.

The server 104 may be any entity or computing device arranged to carryout the method and computing device functions described herein. Further,the server 104 may be configured to send data 108 to or receive data 106from the client device 102. The server 104 may include a location module110 which may be configured to process the data 106 received from theclient device 102 to determine a locations (present and historical)associated with the client device 102.

The data 106 received by the server 104 from the client device 102 maytake various forms. For example, the client device 102 may provideinformation indicative of a location of the client device 102, movementof the client device 102, or inputs from a user of the client device102. The server 104 may then process the data 106 to identify a locationhistory that matches to the received data.

The data 108 sent to the client device 102 from the server 104 may takevarious forms. For example, the server 104 may send to the client device102 an indication of location, updated location history information, orinformation based on the locations of the device.

FIG. 2 illustrates a schematic drawing of an example device 200. In FIG.2, the computing device takes a form of a client device 200. In someexamples, some components illustrated in FIG. 2 may be distributedacross multiple computing devices. However, for the sake of example, thecomponents are shown and described as part of one example client device200. The client device 200 may be or include a mobile device, desktopcomputer, email/messaging device, tablet computer, or similar devicethat may be configured to perform the functions described herein.

In some implementations, the client device 200 may include a deviceplatform (not shown), which may be configured as a multi-layered Linuxplatform. The device platform may include different applications and anapplication framework, as well as various kernels, libraries, andruntime entities. In other examples, other formats or systems mayoperate the client device 200 as well.

The client device 200 may include an interface 202, a wirelesscommunication component 204, a cellular radio communication component206, a global position system (GPS) 208, sensor(s) 210, data storage212, and a processor 214. Components illustrated in FIG. 2 may be linkedtogether by a communication link 216. The client device 200 may alsoinclude hardware to enable communication within the client device 200and between the client device 200 and another computing device (notshown), such as a server entity. The hardware may include transmitters,receivers, and antennas, for example.

The interface 202 may be configured to allow the client device 200 tocommunicate with another computing device (not shown), such as a server.Thus, the interface 202 may be configured to receive input data from oneor more computing devices, and may also be configured to send outputdata to the one or more computing devices. In some examples, theinterface 202 may also maintain and manage records of data received andsent by the client device 200. In other examples, records of data may bemaintained and managed by other components of the client device 200. Theinterface 202 may also include a receiver and transmitter to receive andsend data. In other examples, the interface 202 may also include auser-interface, such as a keyboard, microphone, touchscreen, etc., toreceive inputs as well.

The wireless communication component 204 may be a communicationinterface that is configured to facilitate wireless data communicationfor the client device 200 according to one or more wirelesscommunication standards. For example, the wireless communicationcomponent 204 may include a Wi-Fi communication component that isconfigured to facilitate wireless data communication according to one ormore IEEE 802.11 standards. As another example, the wirelesscommunication component 204 may include a Bluetooth communicationcomponent that is configured to facilitate wireless data communicationaccording to one or more Bluetooth standards. Other examples are alsopossible.

The processor 214 may be configured to determine one or moregeographical location estimates of the client device 200 using one ormore location-determination components, such as the wirelesscommunication component 204, the cellular radio communication component206, or the GPS 208. For instance, the processor 214 may use alocation-determination algorithm to determine a location of the clientdevice 200 based on a presence and/or location of one or more knownwireless access points within a wireless range of the client device 200.In one example, the wireless communication component 204 may determinethe identity of one or more wireless access points (e.g., a MAC address)and measure an intensity of signals received (e.g., received signalstrength indication) from each of the one or more wireless accesspoints. The received signal strength indication (RSSI) from each uniquewireless access point may be used to determine a distance from eachwireless access point. The distances may then be compared to a databasethat stores information regarding where each unique wireless accesspoint is located. Based on the distance from each wireless access point,and the known location of each of the wireless access point, a locationestimate of the client device 200 may be determined.

In another instance, the processor 214 may use a location-determinationalgorithm to determine a location of the client device 200 based onnearby cellular base stations. For example, the cellular radiocommunication component 206 may be configured to at least identify acell from which the client device 200 is receiving, or last received,signal from a cellular network. The cellular radio communicationcomponent 206 may also be configured to measure a round trip time (RTT)to a base station providing the signal, and combine this informationwith the identified cell to determine a location estimate. In anotherexample, the cellular communication component 206 may be configured touse observed time difference of arrival (OTDOA) from three or more basestations to estimate the location of the client device 200.

In still another instance, the processor 214 may use alocation-determination algorithm to determine a location of the clientdevice 200 based on signals sent by GPS satellites above the Earth. Forexample, the GPS 208 may be configured to estimate a location of themobile device by precisely timing signals sent by the GPS satellites.

In some examples, the processor 214 may use a location-determinationalgorithm that combines location estimates determined by multiplelocation-determination components, such as a combination of the wirelesscommunication component 204, the cellular radio component 206, and theGPS 208.

The sensor 210 may include one or more sensors, or may represent one ormore sensors included within the client device 200. Example sensorsinclude an accelerometer, gyroscope, pedometer, light sensors,microphone, camera, or other location and/or context-aware sensors.

The data storage 212 may store program logic 218 that can be accessedand executed by the processor 214. The data storage 210 may also storecollected sensor data 220 that may include data collected by any of thewireless communication component 204, the cellular radio communicationcomponent 206, the GPS 208, and any of sensors 210.

The communication link 216 is illustrated as a wired connection;however, wireless connections may also be used. For example, thecommunication link 216 may be a wired serial bus such as a universalserial bus or a parallel bus, or a wireless connection using, e.g.,short-range wireless radio technology, communication protocols describedin IEEE 802.11 (including any IEEE 802.11 revisions), or Cellulartechnology, among other possibilities.

The client device 200 is illustrated to include an additional processor222. The processor 222 may be configured to control other aspects of theclient device 200 including displays or outputs of the client device 200(e.g., the processor 222 may be a GPU). Example methods described hereinmay be performed individually by components of the client device 200, orin combination by one or all of the components of the client device 200.In one instance, portions of the client device 200 may process data andprovide an output internally in the client device 200 to the processor222, for example. In other instances, portions of the client device 200may process data and provide outputs externally to other computingdevices.

FIG. 3 illustrates a schematic drawing of another example computingdevice. In FIG. 3, the computing device takes a form of a server 300. Insome examples, some components illustrated in FIG. 3 may be distributedacross multiple servers. However, for the sake of example, thecomponents are shown and described as part of one example server 300.The server 300 may be a computing device, cloud, or similar entity thatmay be configured to perform the functions described herein.

The server 300 may include a communication interface 302, a locationmodule 304, a processor 306, and data storage 308. All of the componentsillustrated in FIG. 3 may be linked together by a communication link 310(e.g., wired or wireless link). The server 300 may also include hardwareto enable communication within the server 300 and between the server 300and another computing device (not shown). The hardware may includetransmitters, receivers, and antennas, for example.

The communication interface 302 may allow the server 300 to communicatewith another device (not shown), such as a mobile phone, personalcomputer, etc. Thus, the communication interface 302 may be configuredto receive input data from one or more computing devices, and may alsobe configured to send output data to the one or more computing devices.In some examples, the communication interface 302 may also maintain andmanage records of data received and sent by the server 300. In otherexamples, records of data may be maintained and managed by othercomponents of the server 300.

The location module 304 may be configured to receive data from a clientdevice and determine a geographic location of the client device. Thedetermination may be based on outputs of an accelerometer, gyroscope, orother sensors of the client device, as well as based on locationdeterminations of the client device. The location module 304 may furtherbe configured to determine and store a history of sensor measurements ofthe client device for later reprocessing based on updated datapertaining to networks or information used to the determine thelocations.

The data storage 308 may store program logic 312 that can be accessedand executed by the processor 306. The data storage 310 may also includea location database 314 that can be accessed by the processor 306 aswell, for example, to retrieve information regarding wireless accesspoints, locations of satellites in a GPS network, floor plans of abuilding, etc., or any other type of information useful for determininga location of a client device.

The server is illustrated with a second processor 316 which may be anapplication specific processor for input/output functionality. In otherexamples, functions of the processor 306 and the processor 316 may becombined into one component.

Within examples, measurements collected from various sensors of a device(such as WiFi components, GPS sensors, and inertial sensors) can becombined with information from external databases (such as knownlocations of WiFi access points or building floor plans) to estimate alocation or movement of the device in real-time. Recording the real-timelocation estimate at all times (or intervals/increments of time) mayalso produce a location history.

FIG. 4 is a flow diagram illustrating an example method for determininga location or movement of a device. Initially, computing device(s) 400,operated by users 402 or surveyors 404, may traverse areas in anenvironment and output traces to a model builder 406. A device operatedby a user 402 may output traces passively (i.e., the device may beconfigured to output the trace data with no additional user input),including raw data output by sensors of the device like WiFi scans, GPSdata, accelerometer data, etc. Each trace may be associated with a timethe data was collected, and thus, for traces that include GPS data,other data in the traces also has location-specific references. A deviceoperated by a surveyor 404 may have location-specific references for alltraces, whether due to associated GPS data or manual input of locationinformation.

The model builder 406 may be a module on a computing device or server,and may be configured to generate a model of the environment based onthe received traces. The model builder 406 may include a trace localizerand a map builder. The model builder 406 may access reference data suchas information like strength of signal (RSSI) for WiFi access points inthe environment at specific locations in the environment, or otherlandmark data of the environment. The model builder 406 may beconfigured to generate a map or path of the device based on the traces.In one example, the model builder 406 may utilize GPS data to determinelocations of the device over time, utilize dead reckoning (based onaccelerometer and gyroscope outputs) to project a path, and optimize thepath by jointly combining each. The model builder 406 may furtheroptimize the path to match WiFi scan data to the reference WiFi maps toalign a path that most likely resembles a path that the device traversedthrough the environment.

A location provider 408 may access a model output by the model builder406 to determine locations of other device(s) 410 based on providedpassive traces as well. Within examples, the location provider 408 mayreturn a location of the device or an estimation of movement of thedevice to the device 410 based on data received in the traces.

Traces received from devices may include a variety of measurements frommultiple different sensors, and may include a variety of measurementscollected over time or at various locations. A trace may refer to asensor log or a collection of data output from sensors on the deviceover some time period. The sensors that output data may be selected, ordata to be included within the sensor log may also be selected. In someexamples, a trace of data may include all data collected by a device(using a number of sensors) over a given time frame (e.g., about 5seconds, or perhaps about 5 minutes long). Measurements in a trace orfrom trace to trace may be considered statistically independent.However, in instances in which the measurements are collected frompositions/locations in close proximity or collected close in time, themeasurements may have correlations. To reflect the fact that time orposition can influence measurement noises, information from measurementsthat are close in time or in space can be discounted so as todown-weight information received from each measurement, such that when asame or similar data measurement is observed more than once thatsatisfies the time or position correlation, the measurements can beassigned weights to discount the information. By discounting correlatedmeasurements, errors in such measurements may not be considered moreheavily within estimations of location or movement of the device. Also,however, by discounting correlated measurements, all informationincluding erroneous noise signals as well as correct data measurements,are discounted, and thus some information may be lost.

FIG. 5 is a block diagram of an example method of determining errorcomponents of an estimation of position of a computing device, inaccordance with at least some embodiments described herein. Method 500shown in FIG. 5 presents an embodiment of a method that, for example,could be used with the system 100 in FIG. 1, the device 200 in FIG. 2,the server 300 in FIG. 3, or the system in FIG. 4, for example, or maybe performed by a combination of any components of in FIGS. 1-4. Method500 may include one or more operations, functions, or actions asillustrated by one or more of blocks 502-512. Although the blocks areillustrated in a sequential order, these blocks may in some instances beperformed in parallel, and/or in a different order than those describedherein. Also, the various blocks may be combined into fewer blocks,divided into additional blocks, and/or removed based upon the desiredimplementation.

In addition, for the method 500 and other processes and methodsdisclosed herein, the flowchart shows functionality and operation of onepossible implementation of present embodiments. In this regard, eachblock may represent a module, a segment, or a portion of program code,which includes one or more instructions executable by a processor forimplementing specific logical functions or steps in the process. Theprogram code may be stored on any type of computer readable medium, forexample, such as a storage device including a disk or hard drive. Thecomputer readable medium may include a non-transitory computer readablemedium, for example, such as computer-readable media that stores datafor short periods of time like register memory, processor cache andRandom Access Memory (RAM). The computer readable medium may alsoinclude non-transitory media, such as secondary or persistent long termstorage, like read only memory (ROM), optical or magnetic disks,compact-disc read only memory (CD-ROM), for example. The computerreadable media may also be any other volatile or non-volatile storagesystems. The computer readable medium may be considered a computerreadable storage medium, a tangible storage device, or other article ofmanufacture, for example.

In addition, for the method 500 and other processes and methodsdisclosed herein, each block in FIG. 5 may represent circuitry that iswired to perform the specific logical functions in the process.

Functions of the method 500 may be fully performed by a computing device(or components of a computing device), or may be distributed acrossmultiple computing devices and/or a server. In some examples, thecomputing device may receive information from sensors of the computingdevice, or where the computing device is a server the information can bereceived from another device that collects the information. Thecomputing device could further communicate with a server to determinethe matching media files, for example.

At block 502, the method 500 includes determining an estimation of acurrent position of the computing device based on a previous position ofthe computing device, an estimated speed over an elapsed time, and adirection of travel of the computing device. Within examples,information indicating a previous position may be received from a serverthat calculates or determines the information due to communication withthe computing device, or from sensors of the computing device includinga GPS sensor. The previous position may also be derived or calculatedfrom a number of data points such as GPS location determinations, orWiFi scans and associated WiFi mappings.

The estimated speed can also be received from a server, or derived orcalculated from position determinations over the elapsed time or basedon other data over the elapsed time including outputs of a pedometer,for example. Using a known or estimated distance traveled (as derived orcalculated from outputs of a pedometer, outputs of an accelerometerinferring a step has been taken, or other sensor data), a speed can bedetermined based on the elapsed time.

The direction of travel of the computing device may similarly bedetermined from data received from a server, or from sensors on-boardthe computing device such as a magnetometer or compass, for example. Anyavailable information may be used to infer a direction of travelincluding a fusion of accelerometer, gyroscope, and optionallymagnetometer data, for example. In still other examples, other availableinformation can be used to provide further estimates (directly orindirectly) as to direction of travel, including WiFi scans received intraces that may give information as to a position and heading of adevice and/or user.

The estimation of the current position of the computing device can bedetermined based on a dead reckoning calculation. As an example, anaccelerometer of the computing device can be used as a pedometer and amagnetometer as a compass heading provider. Each step of a user of thecomputing device causes a position to move forward a fixed distance in adirection measured by the compass. Accuracy may be limited by precisionof the sensors, magnetic disturbances inside structures of the computingdevice, and unknown variables such as carrying position of the computingdevice and stride length of the user. However, the estimate of thecurrent position can be determined in this manner.

At block 504, the method 500 includes determining a forward errorcomponent of the estimation of the current position of the computingdevice. The forward error component is indicative of error in theestimation of the current position along a forward direction of travelof the computing device. The forward error component is representativeof an error vector between the estimation of the current position of thecomputing device based on dead reckoning and an estimation of thecurrent position of the computing device based on all availableinformation (e.g., GPS, WiFi scans, etc.) along the forward direction oftravel of the computing device.

In some examples, the forward error component is determined based oncomparison of the estimation of the current position of the computingdevice with a projection of a current position of the computing deviceusing an average human step length. For example, an average human steplength can be used to project where the computing device is currentlypositioned based on the previous position. As one example, an averagehuman step length may be between about 50 cm to about 1 meter, or may bedependent upon a specific user of the computing device (e.g., such asage, gender, height, and weight). The current position estimation mayindicate a position a forward distance of about 1 meter from theprevious position, while the average human step length used may be 50cm. Thus, in this example, the forward error component may be a vectorof about 50 cm, which covers an area of possible error in the forwarddirection of the estimated current position. The average human (or user)step length can also be a variable optimized over time. In this specificexample, the forward error may be computed using a specific user'saverage step length instead of the average human step length. Thus,within examples, a specific user's average step length may be known orlearned over time, and used instead of a generic average step length ofall humans (based on data collected over time from many individuals).

At block 506, the method 500 includes determining a sideways errorcomponent of the estimation of the current position of the computingdevice. The sideways error component is indicative of error in theestimation of the current position along a sideways direction that issubstantially perpendicular to the direction of travel of the computingdevice. The sideways error component is representative of an errorvector between the estimation of the current position of the computingdevice based on dead reckoning and an estimation of current position ofthe computing device based on all available information along thesideways direction that is substantially perpendicular to the directionof travel of the computing device.

In some examples, like the forward error component, the sideways errorcomponent can be determined based on comparison of the estimation of thecurrent position of the computing device with a projection of a currentposition of the computing device using an average human step direction.For example, an average human step direction may be about the samedirection as previously traveled so as to travel in an approximatestraight line. In this example, the projection may result in the userwalking straight. The sideways error component may then be a differencebetween the projected position and the estimated position along a vectorthat is perpendicular to the direction of travel of the computingdevice.

In other examples, an output of a gyroscope or a compass of thecomputing device may be used to determine a direction or change ofdirection of travel. The change of direction can then be used to projecta sideways direction change and sideways error component.

At block 508, the method 500 includes determining an orientation changeerror component of the estimation of the current position of thecomputing device. The orientation change error component is indicativeof error in the estimation of the current position due to a change inthe direction of travel of the computing device (or error in estimatedchange in orientation between previous and current position of thedevice). The orientation change error component is representative of anerror vector between the estimation of the current position of thecomputing device based on dead reckoning and an estimation of currentposition of the computing device based on all available information dueto the change in the direction of travel of the computing device.

In some examples, outputs from a gyroscope or compass of the computingdevice can be received, and changes in the direction of travel of thecomputing device can be based on the outputs from the gyroscope. Theorientation change error component can be determined based on comparisonof the estimation of the current position of the computing device with aprojection of a current position of the computing device taking intoaccount a gyroscope bias. For instance, the current position estimationmay be based on the direction of travel as derived from the gyroscopeoutputs. However, the gyroscope outputs may have some bias associatedwith them due to sensor imperfections, and the bias (when known orcalculated) can be used to project a position of the computing device,and the orientation change error component may be a direction in anangle or heading between the projected and estimated current positions.

At block 510, the method 500 includes determining, by a processor, aweight to apply to the forward error component, the sideways errorcomponent, and the orientation change error component based on averageobserved movement of the computing device. The average observed movementof the computing device may be based on several factors. In someexamples, the movement may be observed over time, and thus, based onspecific movement characteristics of the user of the device (e.g., theuser generally takes strides of about 1 meter, etc.). Average movementmay also be calculated due to a statistical calculation from datareceived from many devices over time, or based on movement of an averagehuman of average size, height, weight, etc. In some examples, dataoutput from one or more sensors of the computing device (e.g., of a GPSsensor, an accelerometer, a gyroscope, a WiFi transceiver, and amagnetometer) indicating movement of the computing device can bereceived over time, and the average observed movement of the computingdevice can be determined based on the data output from the one or moresensors.

The weight to apply to the error components can be different for eacherror component. In some examples, the weight (or a magnitude of theweight) can be chosen as an inverse of an experimental standarddeviation of those errors, so that in average, each error componentrepresents a similar contribution to a cost function. Those weights canalso be derived from noises of the sensors that are used to estimate thespeed and orientation changes, for example. For a component for whichtypical deviations are large, a small can be used, so that the error inthis component does not overly influence changes to the estimatedcurrent position. The weight may be a fraction, when the typicaldeviation of that component is large (or exceeds a threshold like morethan the average step length) so as to reduce effects of this errorcomponent on the position estimation.

In further examples, weights may be determined before looking at theerrors, and rather, based on previous errors seen or observed over manydatasets from other devices. A number of traces from many devices may beused to determine averages of weights to apply over time.

The weight may be useful to prevent an error component from overlyinfluencing changes to updated estimations of the current position ofthe computing device.

At block 512, the method 500 includes using, by the processor, theweighted forward error component, sideways error component, andorientation change error component as constraints for determination ofan updated estimation of the current position of the computing device.For example, the weight can be applied to the forward error component,the sideways error component, and the orientation change error componentto determine the updated estimation of the current position of thecomputing device.

In some examples, additional information may also be received and usedas additional constraints. For instance, a position measurement from aGPS sensor of the computing device can be received and used as a furtherconstraint for determination of the updated estimation of the currentposition of the computing device. In this manner, a dead reckoningconstraint as well as a direct position measurement (GPS) can be used toinfluence updates to the current position estimations. A higher weighton the error components of the dead reckoning estimation enables alarger influence on the estimations.

Within examples, the method 500 in FIG. 5 performs a linearization oferror components of the estimation of the current position of thecomputing device resulting in the forward error component, the sidewayserror component, and the orientation change error component. Applying aweight to the error components helps to capture dynamics of a personwalking to attempt to ensure that segments appear like walking paths,and to provide consistency to the estimations.

The three error components may be considered to provide threeconstraints including one for a gyroscope bias, one for a step length,and one for a step direction, for example. Specifically, a firstconstraint on a device location may include dead reckoning data thatlinks a location of a user between two steps. Using a given example, auser likely takes a step that is about three feet in front of a previousstep, and probably in the same general direction. If the user turned, anangle of turn can be inferred from gyroscope data. Thus, every time auser takes a step, it is expected that the step to be one step forward.A difference between a first position and the resulting expectedposition can be solved using non-linear geometry. However, using methodsdescribed herein, the non-linear geometry can be decomposed into threeseparate linear components to linearize estimate of error along theforward and sideways directions. Further, applying a factor for eachcomponent helps to consider the components in a way that synthesizes auser walking. If a forward error is large, this may simply represent auser taking a large step, however, since large steps are less likely tooccur, a weight can be applied to such a forward component.

FIG. 6 is a conceptual diagram illustrating an example determination oferror components of an estimation of position of a computing device. InFIG. 6, a displacement of the computing device before a movement or stepis labeled as “A (xi, yi)”, and a user's step length is shown. Anestimation of a current position of the computing device after theuser's step based on the previous position of the computing device, anestimated speed over an elapsed time, and a direction of travel of thecomputing device is labeled as “B”, and the currently estimated positionof the computing device after the step based on all information (e.g.,GPS, WiFi, other dead reckoning data) is labeled as “C (xi+1, yi+1)”.Since the actual position or orientation of the device or user is notknown, there is a current estimate, and positions labeled A and C arecurrent estimates of the position of the user at time ti and ti+1. Thoseestimates are based on all data from the device. The position labeled Bis the position estimate of the user at time ti+1 based only on deadreckoning estimates after one step from A. If the dead reckoning andestimates were perfect, B and C would be identical. But errors in thecurrent estimates (A and C) and errors in the dead reckoning (A to B)may cause those two to differ. The errors can thus be determined asrelative (rather than absolute) to the estimated positions.

The forward error component of the estimation of the current position ofthe computing device is labeled with an error vector, and is determinedbased on error in the estimation of the current position along a forwarddirection of travel of the computing device (or a difference betweenposition B and C along a projection of the direction of travel). Thesideways error component of the estimation of the current position ofthe computing device is also labeled with an error vector, is determinedbased on error in the estimation of the current position along asideways direction that is substantially perpendicular to the directionof travel of the computing device (or a perpendicular positiondifference between position B and C).

The orientation change error component of the estimation of the currentposition of the computing device is further labeled with a heading errorvector, and is based on error in the estimation of the current positiondue to a change in the direction of travel of the computing device. Theorientation change error may be computed by a difference between theexpected orientation given the previous orientation and the measuredchange of orientation, and the current orientation estimate, based onall other information.

As shown, the triangles represent arrow heads indicating a direction oftravel of the computing device, and the direction of travel changed fromposition A to C. The direction of travel at position A may be describedby angle θ_(i), the direction of travel at position B is (θ_(i)+Δθ_(i)),and the direction of travel at position C is represented by (θ_(i)+1).The orientation change error component is Δθ_(i), which can bedetermined from the on-board sensors.

A weight may be applied to the forward, sideways, and orientation changeerror components based on average observed movements of the computingdevice, to reduce influence of measurements indicating abnormal movementof the device upon estimations of movement of the device.

FIG. 7 is another conceptual diagram illustrating an exampledetermination of an updated estimation of position of a computingdevice. In FIG. 7, a previous position of a user is labeled as “A”, anda position after a step is labeled as “C” (similar to FIG. 6). Acomputing device of the person may yield number data from whichestimations of the position after the step can be inferred. Exampleestimations of positions after the step are shown as may be determinedfrom dead reckoning (labeled as “B”), WiFi signal maps (labeled as “D”),and GPS (labeled as “E”). Each of the data may have an error associatedwith the estimation, and as described above, the dead reckoning errorsmay be decomposed into weighted forward, sideways, and orientationchange error components based on average observed movement of thecomputing device. Using this method, the weighted dead reckoning errorcomponents, and the other data indicating other estimations of position,may be utilized as constraints for determination of an updatedestimation of the current position of the computing device. In FIG. 7,each estimation at positions B, D, and E are shown to pull theestimation of position closer to the position at C. The current estimateof the device according to all information available is represented byposition C.

It should be understood that arrangements described herein are forpurposes of example only. As such, those skilled in the art willappreciate that other arrangements and other elements (e.g. machines,interfaces, functions, orders, and groupings of functions, etc.) can beused instead, and some elements may be omitted altogether according tothe desired results. Further, many of the elements that are describedare functional entities that may be implemented as discrete ordistributed components or in conjunction with other components, in anysuitable combination and location, or other structural elementsdescribed as independent structures may be combined.

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purposes ofillustration and are not intended to be limiting, with the true scopebeing indicated by the following claims, along with the full scope ofequivalents to which such claims are entitled. It is also to beunderstood that the terminology used herein is for the purpose ofdescribing particular embodiments only, and is not intended to belimiting.

What is claimed is:
 1. A method performed by one or more processorsexecuting instructions stored in memory, comprising: determining anestimation of a current position of a computing device; determining aforward error component of the estimation of the current position of thecomputing device, wherein the forward error component is indicative oferror in the estimation of the current position along a forwarddirection of travel of the computing device; determining a sidewayserror component of the estimation of the current position of thecomputing device, wherein the sideways error component is indicative oferror in the estimation of the current position along a sidewaysdirection that is substantially perpendicular to the direction of travelof the computing device; determining a weight to apply to the forwarderror component and determining a weight to apply to the sideways errorcomponent, in each case based on average observed movement of thecomputing device; and using the weighted forward error component and theweighted sideways error component as constraints for determination of anupdated estimation of the current position of the computing device. 2.The method of claim 1, wherein determining the estimation of the currentposition of the computing device comprises determining the estimationbased on a previous position of the computing device, an estimated speedover an elapsed time, and a direction of travel of the computing device.3. The method of claim 1, further comprising: determining an orientationchange error component of the estimation of the current position of thecomputing device, wherein the orientation change error component isindicative of error in the estimation of the current position due to achange in the direction of travel of the computing device; determining aweight to apply to the orientation change error component based onaverage observed movement of the computing device; and using theweighted orientation change error component as another constraint fordetermination of the updated estimation of the current position of thecomputing device.
 4. The method of claim 3, further comprising:determining the orientation change error component of the estimation ofthe current position of the computing device based on comparison of theestimation of the current position of the computing device with aprojection of a current position of the computing device taking intoaccount a gyroscope bias.
 5. The method of claim 1, wherein the forwarderror component is representative of an error vector between theestimation of the current position of the computing device based on adead reckoning calculation and an estimation of current position of thecomputing device based on additional sensor information output by thecomputing device along the forward direction of travel of the computingdevice.
 6. The method of claim 1, wherein the sideways error componentis representative of an error vector between the estimation of thecurrent position of the computing device based on a dead reckoningcalculation and an estimation of current position of the computingdevice based on additional sensor information output by the computingdevice along the sideways direction that is substantially perpendicularto the direction of travel of the computing device.
 7. The method ofclaim 1, wherein determining the estimation of the current position ofthe computing device comprises: determining the estimation of thecurrent position of the computing device based on a dead reckoningcalculation.
 8. The method of claim 1, further comprising: determiningthe forward error component of the estimation of the current position ofthe computing device based on comparison of the estimation of thecurrent position of the computing device with a projection of a currentposition of the computing device using an average human step length. 9.The method of claim 1, further comprising: determining the sidewayserror component of the estimation of the current position of thecomputing device based on comparison of the estimation of the currentposition of the computing device with a projection of a current positionof the computing device using an average human step direction.
 10. Themethod of claim 1, further comprising: receiving outputs from agyroscope of the computing device; and determining the direction oftravel of the computing device based on the outputs from the gyroscope.11. The method of claim 1, further comprising applying the weight to theforward error component and the sideways error component to determinethe updated estimation of the current position of the computing device.12. The method of claim 1, further comprising: receiving data outputfrom one or more sensors of the computing device indicative of movementof the computing device, wherein the one or more sensors include one ormore of a GPS sensor, an accelerometer, a gyroscope, a WiFi transceiver,and a magnetometer; and determining the average observed movement of thecomputing device based on the data output from the one or more sensors.13. The method of claim 1, wherein determining the weight to apply tothe forward error component and the sideways error component comprises:determining a magnitude of the weight to be inversely proportional to astandard deviation of the estimation of the current position of thecomputing device to a projection of a current position of the computingdevice using an average human step length and step direction.
 14. Themethod of claim 1, further comprising: receiving a position measurementfrom a GPS sensor of the computing device; and using the positionmeasurement as a further constraint for determination of the updatedestimation of the current position of the computing device.
 15. Acomputer readable memory having stored therein instructions, that whenexecuted by a computing device, cause the computing device to performfunctions comprising: determining an estimation of a current position ofthe computing device; determining a forward error component of theestimation of the current position of the computing device, wherein theforward error component is indicative of error in the estimation of thecurrent position along a forward direction of travel of the computingdevice; determining a sideways error component of the estimation of thecurrent position of the computing device, wherein the sideways errorcomponent is indicative of error in the estimation of the currentposition along a sideways direction that is substantially perpendicularto the direction of travel of the computing device; determining a weightto apply to the forward error component and determining a weight toapply to the sideways error component, in each case based on averageobserved movement of the computing device; and using the weightedforward error component and the weighted sideways error component asconstraints for determination of an updated estimation of the currentposition of the computing device.
 16. The computer readable memory ofclaim 15, wherein determining the estimation of the current position ofthe computing device comprises: determining the estimation of thecurrent position of the computing device based on a dead reckoningcalculation.
 17. The computer readable memory of claim 15, wherein thefunctions further comprise: determining the forward error component ofthe estimation of the current position of the computing device based oncomparison of the estimation of the current position of the computingdevice with a projection of a current position of the computing deviceusing an average human step length; and determining the sideways errorcomponent of the estimation of the current position of the computingdevice based on comparison of the estimation of the current position ofthe computing device with the projection of the current position of thecomputing device using an average human step direction.
 18. A computingdevice comprising: one or more processors; and data storage configuredto store instructions that, when executed by the one or more processors,cause the computing device to perform functions comprising: determiningan estimation of a current position of the computing device; determininga forward error component of the estimation of the current position ofthe computing device, wherein the forward error component is indicativeof error in the estimation of the current position along a forwarddirection of travel of the computing device; determining a sidewayserror component of the estimation of the current position of thecomputing device, wherein the sideways error component is indicative oferror in the estimation of the current position along a sidewaysdirection that is substantially perpendicular to the direction of travelof the computing device; determining a weight to apply to the forwarderror component and determining a weight to apply to the sideways errorcomponent, in each case based on average observed movement of thecomputing device; and using the weighted forward error component and theweighted sideways error component as constraints for determination of anupdated estimation of the current position of the computing device. 19.The computing device of claim 18, wherein determining the estimation ofthe current position of the computing device comprises determining theestimation of the current position of the computing device based on adead reckoning calculation.
 20. The computing device of claim 19,wherein determining the forward error component and the sideways errorcomponent comprises comparing the dead reckoning calculation with aprojection of a current position of the computing device using anaverage step length of a user associated with the computing device.